Predicting behavior change from persuasive messages using neural representational similarity and social network analyses

Abstract

Neural activity in medial prefrontal cortex (MPFC), identified as engaging in self-related processing, predicts later health behavior change. However, it is unknown to what extent individual differences in neural representation of content and lived experience influence this brain-behavior relationship. We examined whether the strength of content-specific representations during persuasive messaging relates to later behavior change, and whether these relationships change as a function of individuals' social network composition. In our study, smokers viewed anti-smoking messages while undergoing fMRI and we measured changes in their smoking behavior one month later. Using representational similarity analyses, we found that the degree to which message content (i.e. health, social, or valence information) was represented in a self-related processing MPFC region was associated with later smoking behavior, with increased representations of negatively valenced (risk) information corresponding to greater message-consistent behavior change. Furthermore, the relationship between representations and behavior change depended on social network composition: smokers who had proportionally fewer smokers in their network showed increases in smoking behavior when social or health content was strongly represented in MPFC, whereas message-consistent behavior (i.e., less smoking) was more likely for those with proportionally more smokers in their social network who represented social or health consequences more strongly. These results highlight the dynamic relationship between representations in MPFC and key outcomes such as health behavior change; a complete understanding of the role of MPFC in motivation and action should take into account individual differences in neural representation of stimulus attributes and social context variables such as social network composition.

Keywords: FMRI; Health behavior; Motivation; Multivariate analyses; RSA; Smoking.

Copyright © 2017 Elsevier Inc. All rights reserved.

Figures

Figure 1

Diagram of RSA Analysis Method

Figure 2

Univariate Valence Representation Predicts Behavior Change. As univariate representations of valence increase, smoking decreases. Each data point represents one subject (n = 40). The x-axis displays the degree to which a subjects’ neural RDM correlated with the valence model RDM (all scores were Fisher r-to-z transformed). The y-axis displays the degree to which a subject changed smoking behavior (negative numbers indicate an overall proportion reduction in smoking.)

Figure 3

Multivariate Social Representation Predicts Behavior Change. As multivariate representations of social information increase, participants reduce their smoking less. Each data point represents one subject (n = 40). The x-axis displays the degree to which a subjects’ neuralRDM correlated with the social model RDM (all scores were Fisher r-to-z transformed). The y-axis displays the degree to which a subject changed smoking behavior (negative numbers indicate an overall proportion reduction in smoking.)

Figure 4

Interaction Plots for Social Network Analysis (residuals). Low relative proportions of smokers in participant social networks were associated with stronger relationships between multivariate representations of social and health information and smoking change. (For visualization purposes, subjects were divided by network composition into approximately equal groups, though statistics presented in the main paper use continuous variables. Low = less than/equal to 24%; High = greater than 49%)